{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Use below for charts in dark jupyter theme\n",
    "\n",
    "THEME_DARK = False\n",
    "\n",
    "if THEME_DARK:\n",
    "    # This is used if Jupyter Theme dark is enabled. \n",
    "    # The theme chosen can be activated with jupyter theme as follows:\n",
    "    # >>> jt -t oceans16 -T -nfs 115 -cellw 98% -N  -kl -ofs 11 -altmd\n",
    "    font_size = '20.0'\n",
    "    dark_theme_config = {\n",
    "        \"ytick.color\" : \"w\",\n",
    "        \"xtick.color\" : \"w\",\n",
    "        \"text.color\": \"white\",\n",
    "        'font.size': font_size,\n",
    "        'axes.titlesize': font_size,\n",
    "        'axes.labelsize': font_size, \n",
    "        'xtick.labelsize': font_size, \n",
    "        'ytick.labelsize': font_size, \n",
    "        'legend.fontsize': font_size, \n",
    "        'figure.titlesize': font_size,\n",
    "        'figure.figsize': [20, 7],\n",
    "        'figure.facecolor': \"#384151\",\n",
    "        'legend.facecolor': \"#384151\",\n",
    "        \"axes.labelcolor\" : \"w\",\n",
    "        \"axes.edgecolor\" : \"w\"\n",
    "    }\n",
    "    plt.rcParams.update(dark_theme_config)\n",
    "\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import xai\n",
    "import xai.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_path = 'data/adult.data'\n",
    "categorical_cols = [\"gender\", \"workclass\", \"education\", \"education-num\", \"marital-status\",\n",
    "                   \"occupation\", \"relationship\", \"ethnicity\", \"loan\"]\n",
    "csv_columns = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\",\n",
    "                   \"occupation\", \"relationship\", \"ethnicity\", \"gender\", \"capital-gain\", \"capital-loss\",\n",
    "                   \"hours-per-week\", \"loan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      workclass    education  education-num       marital-status  \\\n",
       "32556   27        Private   Assoc-acdm             12   Married-civ-spouse   \n",
       "32557   40        Private      HS-grad              9   Married-civ-spouse   \n",
       "32558   58        Private      HS-grad              9              Widowed   \n",
       "32559   22        Private      HS-grad              9        Never-married   \n",
       "32560   52   Self-emp-inc      HS-grad              9   Married-civ-spouse   \n",
       "\n",
       "               occupation relationship ethnicity   gender  capital-gain  \\\n",
       "32556        Tech-support         Wife     White   Female             0   \n",
       "32557   Machine-op-inspct      Husband     White     Male             0   \n",
       "32558        Adm-clerical    Unmarried     White   Female             0   \n",
       "32559        Adm-clerical    Own-child     White     Male             0   \n",
       "32560     Exec-managerial         Wife     White   Female         15024   \n",
       "\n",
       "       capital-loss  hours-per-week    loan  \n",
       "32556             0              38   <=50K  \n",
       "32557             0              40    >50K  \n",
       "32558             0              40   <=50K  \n",
       "32559             0              20   <=50K  \n",
       "32560             0              40    >50K  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = xai.data.load_census()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"loan\"\n",
    "protected = [\"ethnicity\", \"gender\", \"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GhZeL/OGdIUOGkJCQQHp6Ojt37gTgrbfeYuTIkSQmJtK8eXOuvPJKADZu3MjHH39M3759gdAPCEVOAhc57baISFVUGA7BmUNPAhucc7+LeGgxMBqYHlwvimifaGY5hAafvwgCZBnwPxGD0P2A/3bO7TWzL82sG6HDVaOAR6ph22pdtHmqjh49SlJSUnh21WNFTrkduX60PTPnHG3btuW9996L2lfk1N8iIlVRmTGHHsCPgCvN7KPgMohQKPQ1s3ygb3AfYAnwGbAJmA3cChAMRE8DVgeXe0sHp4EJwP8G63xKdQxGx0DPnj155ZVXKC4u5sCBA/zlL3/hW9/6Fi1btgxPie2c4+9///tx+7n88svJycnhm2++obCwkDfeeAOAVq1asXv37nA4HDlyhHXr1tXsRolIXKrM2UrvEH1cAMA7fSc4S+m2cvqaA8yJ0p4H+JP2n2JKf8y+Q4cOXHDBBWRlZXHmmWcyf/58JkyYwH333ceRI0cYMWIEHTp0KLefa6+9ltdff52MjIzwbzxDaK6dBQsWMGnSJL744gtKSkqYPHly1CmpRUSqQlN2V7MDBw5wxhlncPDgQS6//HJmzZpV5rcSRKqLpuyuW063Kbt1Mnw1Gz9+POvXr6e4uJjRo0crGETklKRwqGbPPvtsrEsQEakyTbwnIiKe0y4cTtUxFJETode51LTTKhwaNWpEUVGR/uPIac05R1FREY0aNYp1KXIaO63GHFJSUigoKGD37t2xLkWkRjVq1IiUlJSKFxQ5SadVONSvX5+WLVvGugwRkVPeaXVYSUREqofCQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxVBgOZjbHzHaZ2ccRbfeY2XYz+yi4DIp47L/NbJOZbTSz/hHtA4K2TWY2JaK9pZmtMrN8M3vezBpU5waKiMiJq8yew9PAgCjtv3fOZQaXJQBmlg6MANoG6zxuZolmlgg8BgwE0oGRwbIAM4K+0oDPgZurskEiIlJ1FYaDc+4tYG8l+xsM5DjnDjnnNgObgC7BZZNz7jPn3GEgBxhsZgZcCSwI1p8LDDnBbRARkWpWlTGHiWb2j+Cw01lBWwtgW8QyBUFbee1nA/uccyXHtEdlZuPNLM/M8nbv3l2F0kVE5HhONhxmAt8DMoFC4MGg3aIs606iPSrn3CznXJZzLis5OfnEKhYRkUqrdzIrOed2lt42s9nAq8HdAuC8iEVTgB3B7Wjte4AkM6sX7D1ELi8iIjFyUnsOZtYs4u61QOmZTIuBEWbW0MxaAmnA+8BqIC04M6kBoUHrxc45B7wBDA3WHw0sOpmaRESk+lS452BmzwG9gXPMrACYCvQ2s0xCh4C2AP8J4JxbZ2YvAOuBEuA259w3QT8TgWVAIjDHObcueIo7gRwzuw/4EHiy2rZOREROSoXh4JwbGaW53Ddw59yvgV9HaV8CLInS/hmhs5lERKSO0DekRUTEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEo3AQERGPwkFERDwKBxER8SgcRETEUy/WBZy0jRuhd+9YVyFyypq1YUesSzi99H4h1hVUK+05iIiI59Tdc2jVClasiHUVFTu6PNYVnD4S+sS6gtPK+K6Px7qE08oHK26NdQmVY1apxbTnICIiHoWDiIh4KgwHM5tjZrvM7OOItu+YWa6Z5QfXZwXtZmYPm9kmM/uHmXWMWGd0sHy+mY2OaO9kZmuDdR42q+Q+j4iI1JjK7Dk8DQw4pm0KsNw5lwYsD+4DDATSgst4YCaEwgSYCnQFugBTSwMlWGZ8xHrHPpeIiNSyCsPBOfcWsPeY5sHA3OD2XGBIRPs8F7ISSDKzZkB/INc5t9c59zmQCwwIHvsP59x7zjkHzIvoS0REYuRkxxzOdc4VAgTXTYP2FsC2iOUKgrbjtRdEaRcRkRiq7gHpaOMF7iTao3duNt7M8swsb/fu3SdZooiIVORkw2FncEiI4HpX0F4AnBexXAqwo4L2lCjtUTnnZjnnspxzWcnJySdZuoiIVORkw2ExUHrG0WhgUUT7qOCspW7AF8Fhp2VAPzM7KxiI7gcsCx770sy6BWcpjYroS0REYqTCb0ib2XNAb+AcMysgdNbRdOAFM7sZ2AoMCxZfAgwCNgEHgbEAzrm9ZjYNWB0sd69zrnSQewKhM6IaA38NLiIiEkMVhoNzbmQ5D3lzGQRnHN1WTj9zgDlR2vOAdhXVISIitUffkBYREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxKBxERMSjcBAREY/CQUREPAoHERHxVCkczGyLma01s4/MLC9o+46Z5ZpZfnB9VtBuZvawmW0ys3+YWceIfkYHy+eb2eiqbZKIiFRVdew5XOGcy3TOZQX3pwDLnXNpwPLgPsBAIC24jAdmQihMgKlAV6ALMLU0UEREJDZq4rDSYGBucHsuMCSifZ4LWQkkmVkzoD+Q65zb65z7HMgFBtRAXSIiUklVDQcH/J+ZrTGz8UHbuc65QoDgumnQ3gLYFrFuQdBWXruIiMRIvSqu38M5t8PMmgK5ZvbJcZa1KG3uOO1+B6EAGg9w/vnnn2itIiJSSVXac3DO7QiudwEvExoz2BkcLiK43hUsXgCcF7F6CrDjOO3Rnm+Wcy7LOZeVnJxcldJFROQ4TjoczOzbZtak9DbQD/gYWAyUnnE0GlgU3F4MjArOWuoGfBEcdloG9DOzs4KB6H5Bm4iIxEhVDiudC7xsZqX9POucW2pmq4EXzOxmYCswLFh+CTAI2AQcBMYCOOf2mtk0YHWw3L3Oub1VqEtERKropMPBOfcZ0CFKexHQJ0q7A24rp685wJyTrUVERKqXviEtIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHjqTDiY2QAz22hmm8xsSqzrERGJZ3UiHMwsEXgMGAikAyPNLD22VYmIxK86EQ5AF2CTc+4z59xhIAcYHOOaRETiVl0JhxbAtoj7BUGbiIjEQL1YFxCwKG3OW8hsPDA+uHvAzDbWaFXx4xxgT6yLECnHKfH6NLst1iVU1gWVWaiuhEMBcF7E/RRgx7ELOedmAbNqq6h4YWZ5zrmsWNchEo1en7FRVw4rrQbSzKylmTUARgCLY1yTiEjcqhN7Ds65EjObCCwDEoE5zrl1MS5LRCRu1YlwAHDOLQGWxLqOOKVDdVKX6fUZA+acN+4rIiJxrq6MOYiISB2icBAREY/CIU6ZWWMzaxXrOkSiMbMLzOyq4HZjM2sS65rijcIhDpnZNcBHwNLgfqaZ6dRhqRPMbBywAPhj0JQCLIxdRfFJ4RCf7iE0n9U+AOfcR8CFMaxHJNJtQA9gP4BzLh9oGtOK4pDCIT6VOOe+iHURIuU4FEzACYCZ1SPKdDpSsxQO8eljM7sBSDSzNDN7BPhbrIsSCbxpZr8EGptZX+BF4JUY1xR39D2HOGRm3wLuAvoRmvRwGTDNOVcc08JEADNLAG6m7Ovzf53erGqVwkFERDx1ZvoMqXlm9grHOXbrnMuuxXJEyjCztRz/9dm+FsuJewqH+PJArAsQOY7vx7oA+TcdVhIREY/OVopDwRlKC8xsvZl9VnqJdV0iAGbWzcxWm9kBMztsZt+Y2f5Y1xVvFA7x6SlgJlACXAHMA56JaUUi//YoMBLIBxoDPwYeiWlFcUjhEJ8aO+eWEzqs+C/n3D3AlTGuSSTMObcJSHTOfeOce4rQhxipRRqQjk/Fwbnk+cEv8G1H0xNI3XEw+Lngj8zsN0Ah8O0Y1xR3NCAdh8ysM7ABSAKmAWcCv3HOrYxpYSKEZmQFdgH1gTsIvT4fD/YmpJYoHERExKPDSnHIzLIITZ9xARGvAX3JSGLJzP5xvMf1+qxdCof4NB/4BbAWOBrjWkRKHSX0DelnCU2093Vsy4lvOqwUh8zsHedcz1jXIXIsM2tN6DTWa4D1hILi/5xzJTEtLA4pHOKQmfUh9B9wOXCotN059+eYFSVyDDO7HngMmOGc+22s64k3OqwUn8YCrQmdDVJ6WMkBCgeJKTNrAYwArgU+J3S20ssxLSpOac8hDpnZWudcRqzrEIlkZm8CTYAXCP2G9N7Ix51ze6OtJzVD4RCHzGw28Hvn3PpY1yJSysy28O8puyPfmAxwzrmLar2oOKZwiENmtgH4HrCZ0JhD6X8+nSooIoDCIS4F30D1OOf+Vdu1iEjdpIn34lAQAucBVwa3D6LXgohE0J5DHDKzqUAW0Mo5d7GZNQdedM71iHFpIlJH6NNifLoWyAa+AnDO7SB0loiICKBwiFeHXWiX0QGYmaZDFpEyFA7x6QUz+yOQZGbjgNeA2TGuSUTqEI05xCkz6wv0I3Qa6zLnXG6MSxKROkThEEfMrJt+0EdEKkOHleLL46U3zOy9WBYiInWbwiG+WMTtRjGrQkTqPM3KGl8SzOwsQh8KSm+HA0MTm4lIKY05xJFgYrOjlN2DKKWJzUQkTOEgIiIejTmIiIhH4SAiIh6Fg4iIeBQOIiLiUTiIiIhH4SAiIh6Fg0gtMLOnzWxorOsQqSyFg0gdZGaavUBiSi9AkWOY2f8DbgS2AXuANcDLwGNAMqHf3B7nnPvEzJ4G9hP62dXvAv/lnFtgZgY8AlwJbCbiW+lm1gn4HXBG0P8Y51yhma0A/gb0ABYDD9b4xoqUQ+EgEsHMsoDrgEsI/f/4gFA4zAJucc7lm1lXQjPcXhms1gzoCbQm9Ka+gNBPsbYCMoBzgfXAHDOrTyg0BjvndpvZ9cCvgZuCvpKcc71qfENFKqBwECmrJ7DIOfc1gJm9QmgG20uBF0M7BAA0jFhnoXPuKLDezM4N2i4HnnPOfQPsMLPXg/ZWQDsgN+grESiM6Ov56t8kkROncBApK9qkhAnAPudcZjnrHCpn/WgTlxmwzjnXvZy+vqq4RJGapwFpkbLeAa4xs0ZmdgZwNaExhs1mNgzAQjpU0M9bwAgzSzSzZsAVQftGINnMugd91TeztjWyJSJVoHAQieCcW01o3ODvwJ+BPOALQgPUN5vZ34F1wOAKunoZyAfWAjOBN4P+DwNDgRlBXx8ROmQlUqdoym6RY5jZGc65A2b2LUJ7AOOdcx/Eui6R2qQxBxHfLDNLJzQQPVfBIPFIew4iIuLRmIOIiHgUDiIi4lE4iIiIR+EgIiIehYOIiHgUDiIi4vn/n3GIM3+otnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_groups = xai.imbalance_plot(df, \"gender\", categorical_cols=categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sPfHEEwwdOrRuds1jjjmG5cuX89RTT3HCCSfU1Su+8c6xxx5LmzZt6NWrF2+//TYA//jHPzjxxBNp27Yt3bp14/DDDwfglVde4cUXX+SII44ACjcQqr0HAdSf+tvMrBRNJoeIOLGJ9d2LlgM4q5F6o4BRDZRPAypiOsqG5qn65JNP6NSpU93sqmsqnnK7ePuGhntEBHvuuSf//Oc/G9xX8dTfZmal8AjpDB100EH89a9/Zfny5XzwwQfcd999bLHFFvTo0aNuSuyI4Pnnn1/rfg4++GDGjh3L6tWrWbBgAY8++ihQmH564cKFdclh5cqVzJo1a8O+KTPbKFV0l5aW7nq63377MWTIEPr06cPnPvc5qqur6dixI7fffjvf/e53ufzyy1m5ciXDhg2rm6q5IccddxyPPPIIvXv3rrvHM8Cmm27KPffcw9lnn83SpUtZtWoV5557LnvuuWdLvUUz20h4yu6MffDBB3zmM5/ho48+4uCDD2bkyJH17pVgtj48ZbdlbV2n7K7oM4c8jBgxgtmzZ7N8+XKGDx/uxGBmZcnJIWN33HFH3iGYmZWs4i5Il2szmdma/F22PFVUcmjfvj2LFi3yfyorexHBokWLaN++fd6h2EaqopqVqqqqqKmpYeHChXmHYlay9u3bU1VV1XRFsw2gopLDJptsQo8ePfIOw8ys7FVUs5KZmWXDycHMzFKcHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUppMDpJGSXpH0otFZb+R9LKkFyT9RVKnonU/ljRX0iuSBhWVD07K5kq6sKi8h6QpkuZIukvSplm+QTMzW3/rcuZwGzB4jbKJwF4RsTfwKvBjAEm9gGHAnsk2N0hqK6ktcD1wJNALODGpC/Ar4OqI6AksAU4v6R2ZmVnJmkwOEfEPYPEaZQ9HxKrk6dNA7dSRQ4GxEbEiIl4H5gL9k7+5EfFaRHwMjAWGShJwOHBPsv1o4NgS35OZmZUoi2sOpwEPJMs7APOL1tUkZY2VbwO8W5RoassbJGmEpGmSpnlabjOzDaek5CDpp8Aq4PbaogaqRTPKGxQRIyOiOiKqu3Tpsr7hmpnZOmr2/RwkDQe+AgyIT2+9VgPsWFStCngrWW6o/D9AJ0ntkrOH4vpmZpaTZp05SBoMXAAMiYiPilZNAIZJ2kxSD6An8AwwFeiZ9EzalMJF6wlJUnkUOD7ZfjgwvnlvxczMsrIuXVnvBP4J7CapRtLpwB+ADsBESTMk3QQQEbOAu4HZwIPAWRGxOjkr+B7wEPAScHdSFwpJ5geS5lK4BnFLpu/QzMzWW5PNShFxYgPFjf6AR8QvgV82UH4/cH8D5a9R6M1kZmathEdIm5lZipODmZmlODmYmVmKk4OZmaU4OZiZWYqTg5mZpTg5mJlZipODmZmlODmYmVmKk4OZmaU4OZiZWYqTg5mZpTg5mJlZipODmZmlODmYmVmKk4OZmaU4OZiZWYqTg5mZpazLPaRHSXpH0otFZZ0lTZQ0J3ncOimXpOskzZX0gqR9i7YZntSfI2l4UXk/STOTba6TpKzfpJmZrZ91OXO4DRi8RtmFwKSI6AlMSp4DHAn0TP5GADdCIZkAFwP7U7hf9MW1CSWpM6JouzVfy8zMWliTySEi/gEsXqN4KDA6WR4NHFtUPiYKngY6SeoKDAImRsTiiFgCTAQGJ+u2ioh/RkQAY4r2ZWZmOWnuNYftI2IBQPK4XVK+AzC/qF5NUra28poGyhskaYSkaZKmLVy4sJmhm5lZU7K+IN3Q9YJoRnmDImJkRFRHRHWXLl2aGaKZmTWlucnh7aRJiOTxnaS8BtixqF4V8FYT5VUNlJuZWY6amxwmALU9joYD44vKT056LR0ALE2anR4CBkraOrkQPRB4KFn3vqQDkl5KJxfty8zMctKuqQqS7gQOBbaVVEOh19GVwN2STgfeAE5Iqt8PHAXMBT4CTgWIiMWSLgOmJvUujYjai9zfpdAjanPggeTPzMxy1GRyiIgTG1k1oIG6AZzVyH5GAaMaKJ8G7NVUHGZm1nKaTA4bk333vyHvEJr07JQz8w7BzDYCnj7DzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUpwczMwspaTkIOk8SbMkvSjpTkntJfWQNEXSHEl3Sdo0qbtZ8nxusr570X5+nJS/ImlQaW/JzMxK1ezkIGkH4GygOiL2AtoCw4BfAVdHRE9gCXB6ssnpwJKI2AW4OqmHpF7JdnsCg4EbJLVtblxmZla6UpuV2gGbS2oHbAEsAA4H7knWjwaOTZaHJs9J1g+QpKR8bESsiIjXgblA/xLjMjOzEjQ7OUTEm8BVwBsUksJSYDrwbkSsSqrVADskyzsA85NtVyX1tykub2CbeiSNkDRN0rSFCxc2N3QzM2tCKc1KW1M46u8BdAO2BI5soGrUbtLIusbK04URIyOiOiKqu3Tpsv5Bm5nZOimlWenLwOsRsTAiVgJ/Br4AdEqamQCqgLeS5RpgR4BkfUdgcXF5A9uYmVkOSkkObwAHSNoiuXYwAJgNPAocn9QZDoxPlickz0nWPxIRkZQPS3oz9QB6As+UEJeZmZWoXdNVGhYRUyTdAzwLrAKeA0YC9wFjJV2elN2SbHIL8EdJcymcMQxL9jNL0t0UEssq4KyIWN3cuMzMrHQqHLyXn+oOHWJav36Z7nPas62/Nat63255h2A5KIfvJvj7WQ40efL0iKhuqp5HSJuZWUqzm5Vyt9tu8Nhjme5yxP43ZLq/DeHZx87MOwTLQTl8N8Hfz7KghjqIpvnMwczMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCylfKfPMDNrplj0x7xDWCfa5lu5vbbPHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCylpOQgqZOkeyS9LOklSQdK6ixpoqQ5yePWSV1Juk7SXEkvSNq3aD/Dk/pzJA0v9U2ZmVlpSj1zuBZ4MCJ2B/oALwEXApMioicwKXkOcCTQM/kbAdwIIKkzcDGwP9AfuLg2oZiZWT6anRwkbQUcDNwCEBEfR8S7wFBgdFJtNHBssjwUGBMFTwOdJHUFBgETI2JxRCwBJgKDmxuXmZmVrpQzh88DC4FbJT0n6WZJWwLbR8QCgORxu6T+DsD8ou1rkrLGylMkjZA0TdK0hQsXlhC6mZmtTSnJoR2wL3BjROwDfMinTUgNaeiu1rGW8nRhxMiIqI6I6i5duqxvvGZmto5KSQ41QE1ETEme30MhWbydNBeRPL5TVH/Hou2rgLfWUm5mZjlpdnKIiH8D8yXtlhQNAGYDE4DaHkfDgfHJ8gTg5KTX0gHA0qTZ6SFgoKStkwvRA5MyMzPLSakT730fuF3SpsBrwKkUEs7dkk4H3gBOSOreDxwFzAU+SuoSEYslXQZMTepdGhGLS4zLzMxKUFJyiIgZQHUDqwY0UDeAsxrZzyhgVCmxmJlZdjxC2szMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLMXJwczMUpwczMwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLKXk5CCpraTnJP0ted5D0hRJcyTdldxfGkmbJc/nJuu7F+3jx0n5K5IGlRqTmZmVJoszh3OAl4qe/wq4OiJ6AkuA05Py04ElEbELcHVSD0m9gGHAnsBg4AZJbTOIy8zMmqmk5CCpCjgauDl5LuBw4J6kymjg2GR5aPKcZP2ApP5QYGxErIiI14G5QP9S4jIzs9KUeuZwDXA+8EnyfBvg3YhYlTyvAXZIlncA5gMk65cm9evKG9imHkkjJE2TNG3hwoUlhm5mZo1pdnKQ9BXgnYiYXlzcQNVoYt3atqlfGDEyIqojorpLly7rFa+Zma27diVs+0VgiKSjgPbAVhTOJDpJapecHVQBbyX1a4AdgRpJ7YCOwOKi8lrF25iZWQ6afeYQET+OiKqI6E7hgvIjEXES8ChwfFJtODA+WZ6QPCdZ/0hERFI+LOnN1APoCTzT3LjMzKx0pZw5NOYCYKyky4HngFuS8luAP0qaS+GMYRhARMySdDcwG1gFnBURqzdAXGZmto4ySQ4R8RjwWLL8Gg30NoqI5cAJjWz/S+CXWcRiZmal8whpMzNLcXIwM7MUJwczM0txcjAzsxQnBzMzS3FyMDOzFCcHMzNLcXIwM7MUJwczM0txcjAzsxQnBzMzS3FyMDOzFCcHMzNLcXIwM7MUJwczM0txcjAzsxQnBzMzS3FyMDOzFCcHMzNLaXZykLSjpEclvSRplqRzkvLOkiZKmpM8bp2US9J1kuZKekHSvkX7Gp7UnyNpeOlvy8zMSlHKmcMq4IcRsQdwAHCWpF7AhcCkiOgJTEqeAxwJ9Ez+RgA3QiGZABcD+wP9gYtrE4qZmeWj2ckhIhZExLPJ8vvAS8AOwFBgdFJtNHBssjwUGBMFTwOdJHUFBgETI2JxRCwBJgKDmxuXmZmVLpNrDpK6A/sAU4DtI2IBFBIIsF1SbQdgftFmNUlZY+UNvc4ISdMkTVu4cGEWoZuZWQNKTg6SPgPcC5wbEe+trWoDZbGW8nRhxMiIqI6I6i5duqx/sGZmtk5KSg6SNqGQGG6PiD8nxW8nzUUkj+8k5TXAjkWbVwFvraXczMxyUkpvJQG3AC9FxO+KVk0AanscDQfGF5WfnPRaOgBYmjQ7PQQMlLR1ciF6YFJmZmY5aVfCtl8EvgXMlDQjKfsJcCVwt6TTgTeAE5J19wNHAXOBj4BTASJisaTLgKlJvUsjYnEJcZmZWYmanRwi4gkavl4AMKCB+gGc1ci+RgGjmhuLmZllyyOkzcwsxcnBzMxSnBzMzCzFycHMzFKcHMzMLKWUrqxmjZrac/e8Q1gn+815Oe8QzFolnzmYmVmKk4OZmaU4OZiZWYqTg5mZpTg5mJlZipODmZmlODmYmVmKk4OZmaU4OZiZWYqTg5mZpTg5mJlZipODmZmltJrkIGmwpFckzZV0Yd7xmJltzFpFcpDUFrgeOBLoBZwoqVe+UZmZbbxaRXIA+gNzI+K1iPgYGAsMzTkmM7ONVmu5n8MOwPyi5zXA/mtWkjQCGJE8/UDSKy0QW6m2Bf6T1c6ks7LaVTnK9LMEQMp0d2Um88/T38+Mv5+cnO3uCj63LpVaS3Jo6H9opAoiRgIjN3w42ZE0LSKq846jEvizzJY/z2xV2ufZWpqVaoAdi55XAW/lFIuZ2UavtSSHqUBPST0kbQoMAybkHJOZ2UarVTQrRcQqSd8DHgLaAqMiYlbOYWWlrJrBWjl/ltny55mtivo8FZFq2jczs41ca2lWMjOzVsTJwczMUpwczMwspVVckDZrjKTtgC8C3YBlwIvAtIj4JNfAzCqcL0hnSFIVhW64X6L+j9l9wAP+QVt3kg4DLgQ6A88B7wDtgV2BnYF7gN9GxHu5BVlGJB0IfJPCd7Mr9b+bf4qIpTmGV5YkVZP+v/73iFica2AZcXLIiKRbKUwD8jdgGvV/zA4D+gEXRsQ/cguyjEj6DfD7iHijgXXtgK8AbSPi3hYPrsxIeoDCoNLxNPzdPAb4XUR4bNE6kHQKcDbwOjCd+p/nFykkiZ839N0tJ04OGZG0V0S8uJb1mwI7RcTcFgyrbEnq2NjRrKT9ImJqS8dUriRtGxFrnfNnXepYgQoTSI2KiGWNrO8LbBMRk1o2smz5gnR2uja2QtKvIuJjJ4b1MknS1msWShoI/DmHeMrZ0Q0VStpE0p0ATgzrZfxaEsMxETGj3BMDODlk6XpJ9f4TSmoj6TagTz4hlbX/CzwqqUttgaT/Ssob/LGzRp2TzGhcR9KWwP3AR/mEVNYmSeq+ZqGk04BrWjyaDcS9lbIzEHhQ0mYR8WdJ7SlcNF1KoU3X1kNE/D9Jy4FHkrOFbwDfAQ6LiHm5Bld+vkzhu9k+Iq5LEu79wKSI8F0X1995wERY6kTnAAAPUUlEQVRJR0XEHABJPwb+Czgk18gy5OSQkYiYJ+nLwENJ98tvAVMi4gc5h1a2IuKPSYJ4DngD+GJELMo5rLITEYuT7+YDkrpRuJHWjRFxXc6hlaWIuF/SCgqf57HAGcB+wMERsSTf6LLjC9IZkbRvstgVGANMBH5duz4ins0jrnIlaSaFe3qIws1JFgIfJs8jIvbOMbyyIumryWIH4HfAJAp3WwQgInwNpxkkHQSMA54Cvh4Ry3MOKVNODhmR9OhaVkdEHN5iwVQASWu9W1VE/G9LxVLukm7WjYmIOK3FgqkAkt7n0wOXzYCVwGo+PXDZKsfwMuPkYK2apO0pjB8J4K2IeDvnkMw2Ck4OGZK0O4X23LofMwrd3l7ONbAylPQVvwnoCLyZFFcB7wJnuplu/UgaBBxL/e/muIh4KNfAypQkAf2p/3k+ExX0g+rkkBFJFwAnUmjLrUmKa6fTGBsRV+YVWzmSNAP4PxExZY3yA4D/GxHuHryOJF1DYfTuGOp/N08G5kTEOXnFVo6S3nM3AHOof+CyC4UDl4fzii1LTg4ZkfQqsGdErFyjfFNgVkT0zCey8iRpTmOfmaS5EbFLS8dUriS9GhG7NlAu4FV/N9ePpJeAI9fsUi2pB3B/ROyRS2AZc1fW7HxCYQKuNS+Udk3W2fp5QNJ9FI525ydlO1I42n0wt6jK03JJ/SPimTXK9wMqqodNC2nHp2dgxd4ENmnhWDYYJ4fsnEth5OQcPv0x24nCqeb3couqTEXE2ZKO5NNrOKLwH/L6iLg/1+DKzynAjZI68OmP2o7Ae8k6Wz+jgKmSxlL/wGUYcEtuUWXMzUoZktSGTy9S1f6YTY2I1bkGZgZI+ixF382I+HfOIZUtSXuQPnCZEBGzcw0sQ04OG5CkzpUyt3tLk7R3RLyQLG8CXEAh8b4IXB4RnhOomSR9hsIF6tci4t2847HWyRPvZUTSz4qWeyUXqKdLmidp/xxDK1e3FS1fSaF57rfA5hS6uNo6knRD0fJBwGwKn+VMSUflFliZkjS4aLmjpJslvSDpjmRcTkVwcsjOV4uWfwOcExE9gK8DV+cTUllT0fIA4NsRMRn4AdA3n5DK1gFFy5cBx0bEYRQmibs0n5DK2n8XLf8W+DeFyTWnUpg1uCL4gvSG0S0iHgCIiGckbZ53QGWoo6TjKBzAbFbbRTgiQpLbQptvq9oBhBHxmqS2eQdU5qojovZg5WpJw3ONJkNODtn5vKQJFI54qyRtUdQuXjHd21rQZGBIsvy0pO0j4u3koqpvTLN+dpf0AoXvZndJW0fEkqQDhb+b6287ST+g8HluJUlFI6MrpjXGySE7Q9d43gbq5ga6seXDKW8RcWoj5f+m0Mxk627NQVkfJI+dgYtaOJZK8P8ozHALMBrYFliYHLjMyC2qjLm3kpUFSdXACxHxcd6xmG0MKuYUqDWRdH7xo5VGUleSOfPzjqXcJfMs1T1a6SQdl3QPrihODhvGsDUerTTDKZy+n5F3IBXg4OSxYm5nmSdJOwN3A9/MO5asOTlsWGq6iq2DbwE/BjZN/jOatRanAb9KHiuKk4O1apIOA16OiP8AtwKn5xySGQBJN+ATKCSHpZIqahp5Jwdr7U7n08nM7gJOSLpgmuXtKOCpiHifwmR8FdXs6f9k1mpJ6kRhdG/tgML3gKcp/Kc0y1vxgctfgKOS+7dUBI9z2DAeSx4fzTOIcpdMCrfLGmXfyimcSnFH8nh7rlGUueTApVNEPA4QEcsl3QMcToXcb8TjHMzMLMXNShmStMWaF6Uk7SRph7xiMgOQtFVy86Tisr6SdssrJmvdnByytRL4s6Qti8pupnCrULM8vQ9cI6lzUdkNjVU2c3LIUDJz6F+Ab0DhrAHoEhHTcg3MNnrJxHB3kAzWkrR7UvxKroFZq+XkkL2bgdpJ406m0DffrDUYRWG0ORTuHV0x9zu27Lm3UsYi4mVJSNoVOBE4KO+YzAAiYr6khZL2A44D9sk7Jmu9fOawYdxC4QzihYhYkncwZkVupnA2+w/fh9vWxslhw7gb6INP2zMnabSkGyXtlXcsZWo8sBoYmXcg1rp5nIOVlaRJZCegf0RckHc8ZrUk/Z1Cj8XrI+JvecdTKicHKwuStoyID/OOw6wxkrpR6LZ+QERcn3c8pXKzkrVqkr4gaTbwUvK8jyT3z7dWQdLmtQMJI+KtiJheCYkBnBys9bsaGAQsAoiI5/n0hjVmuZF0DIV7Rj+YPO8raUK+UWXHycFavYiYv0bR6lwCMavvEqA/8C5ARMwAuucYT6Y8zsFau/mSvgBEMh3y2SRNTGY5WxURS6XKvOGjzxystfsOcBawA1AD9E2em+XtRUn/BbSV1FPS74Gn8g4qK+6tZGbWDJK2AH4KDKRwv/iHgMsiYnmugWXEyWEDkzQa+IhC3+cX846nXCRHYY1+OSPi7BYMpyJVWr98y5avOWx4f6AwaOtbgAdtrTvPZLvhnUzSLz/vQMqJpL+y9gOXIS0YzgbjM4cNxIO2rDWStDmwk6fqbj5Jh6xtfURMbqlYNiQnh4wlPWtuBj4TETsld4b7PxFxZs6hlSVJXSiccfUC2teWR8ThuQVVppJ++VcBm0ZED0l9gUsr5UjXsuXeStnzoK1s3U6h62oP4BfAPGBqngGVsUuo4H75LS3poXSPpNmSXqv9yzuurDg5bAAetJWpbSLiFmBlREyOiNNwG3lzrYqIpXkHUUFuBW4EVgGHAWOAP+YaUYacHLJXb9CWpB/hQVulWJk8LpB0tKR9gKo8AypjFd0vPwebR8QkCs3z/xsRlwAV09zp5JA9D9rK1uWSOgI/BH5E4XrOefmGVLa+D+wJrADuBN4Dzs01ovK2XFIbYI6k70k6Dtgu76Cy4gvSZmbNkNxb5CWgE3AZ0BH4dUQ8nWtgGXFyyIgHbW0YknpQOOLtTtG4HPewWXcbS798y5YHwWXHg7Y2jHEUbrf6V+CTnGMpV1flHUAlaWpa7kpJtj5zsFZN0pSI2D/vOMxqSVoIzKdw3WYKhXmV6ngQnDXIg7aylfSu6Qk8TOFCKgAR8WxuQZUpST2BK0h/Nz+fW1BlSFJb4AjgRGBv4D7gzoiYlWtgGXOzUvZuB+4CjqbQc2k4sDDXiMpbbwrzUh3Op81KQQV1GWxBtwIXUxioeRhwKmsc9VrTImI1hbu/PShpMwpJ4jFJl0bE7/ONLjs+c8iYpOkR0U/SCxGxd1I2OSLWOh+LNUzSy8DeEfFx3rGUu6Lv5syI6J2UPR4RX8o7tnKTJIWjKSSG7sAEYFREvJlnXFnymUP26g3aAt7Cg7ZK8TyFroLv5B1IBajXLx94kwrql99Skmn49wIeAH5RqVPx+8whY5K+AjwO7Aj8HtiKwheoYm483pIkPUahXXcq9a85VESPkJZU6f3yW4qkT4DaGZeLf0AFRERs1fJRZc/JwVq1xqZHrpQeIWatlZNDxjxoK3uSPgf0jIi/J7dmbBsR7+cdV7nYWPrlW7Z8zSF7HrSVIUnfBkYAnYGdKcxZdRMwIM+4ysyBrKVfvllDfOaQMQ/aypakGRTuQTAlIvZJyup621jTNpZ++ZYtz8qavWslXSzpQEn71v7lHVQZW1HcjVVSO9YyT5ClRcTqiHgwIoZTuBfGXAr98r+fc2hlSVKTZ17rUqe1c7NS9jxoK1uTJf0E2FzSEcCZFJrsbD000C//OuDPecZUxh6VdC8wPiLeqC2UtClwEIWBr48Ct+UTXjbcrJQxD9rKVtIv/3RgIIW28oeAm8Nf3HW2Rr/8sZXaL7+lSGoPnAacROH2te9SmI6kLYVpXq5PbsFa1pwcMibpLuD7EeFBWyWQtFPxUZk138bSLz8PkjYBtgWWRcS7eceTJSeHjHnQVjYkPRsR+ybL90bE1/KOyWxj4msO2bs47wAqRPEFPc8aatbCnBwyFhGTGxq0lXdcZSgaWTazFuBmpYwVD9qKiJ2TOfRviggP2loPklZTaCcXsDnwUe0q3E5utsH5zCF7Z5EM2gKIiDmSPPPleooIn21lRJKa6t21LnVs4+JBcNnzoC1rbR6V9H1JOxUXStpU0uFJV9fhOcVmrZTPHLLnQVvW2gym0C//zmRiyDX75V9dCf3yLVu+5pAxD9qy1qyS++VbtpwcMuJBW9lyO7lZvnzNITvjaheSeVesNG4nN8uRrzlkx4O2suV2crMcuVkpI2tM91C3bKVzO7lZy3NyyIgHbZlZJXFyMDOzFF+QNjOzFCcHMzNLcXIwM7MUJ4eMbCw3HTezjYOTQ3Y8aMvMKoZ7K2VkY7npuJltHJwcNgAP2jKzcufkYGZmKb7mYGZmKU4OZmaW4uRg1gySbpN0fAnbnyLpD1nGZJYlJwezFpDcS9ysbDg5WMWT9HNJL0uaKOlOST+StLOkByVNl/S4pN2TurdJuk7SU5Jeqz07UMEfJM2WdB+wXdH++0manOzrIUldk/LHJP23pMnAOWuJ73OSJkl6IXncKSk/RtIUSc9J+ruk7ZPySySNSvb/mqSzN9ynZxsrJweraJKqga8B+wBfBaqTVSOB70dEP+BHwA1Fm3UFDgK+AlyZlB0H7Ab0Br4NfCHZ/ybA74Hjk32NAn5ZtK9OEXFIRPx2LWH+ARgTEXsDtwPXJeVPAAdExD7AWOD8om12BwYB/YGLkzjMMuNTXat0BwHjI2IZgKS/Uhic+AXgf4pmNNmsaJtxEfEJMLv2aB04GLgzIlYDb0l6JCnfDdgLmJjsqy2woGhfd61DjAdSSFwAfwR+nSxXAXclZyKbAq8XbXNfRKwAVkh6B9geqFmH1zJbJ04OVukams+qDfBuRPRtZJsVjWzf0KAgAbMi4sBG9vVh0yGm1L7O74HfRcQESYcClzQS42r8f9ky5mYlq3RPAMdIai/pM8DRFO7S97qkE6DuekKfJvbzD2CYpLbJkfxhSfkrQBdJByb72kTSng3tQNJxkq5oYNVTwLBk+aQkZoCOwJvJsuflshbl5GAVLSKmAhOA54E/A9OApRR+hE+X9DwwCxjaxK7+AswBZgI3ApOT/X8MHA/8KtnXDJLrEQ3YGXivgfKzgVMlvQB8i08vXl9CoenrceA/Tb1Xsyx5+gyreJI+ExEfSNqCwhnAiIh4Noc4/gScFxELW/q1zdaXk4NVPEl3AL0oXIgeHRENNe2YWREnBzMzS/E1BzMzS3FyMDOzFCcHMzNLcXIwM7MUJwczM0v5/+J+5aBWfq/dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = xai.imbalance_plot(df, \"gender\", \"loan\", categorical_cols=categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bal_df = xai.balance(df, \"gender\", \"loan\", upsample=0.8, categorical_cols=categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(' Female', ' <=50K')\n",
      "4      <=50K\n",
      "5      <=50K\n",
      "6      <=50K\n",
      "12     <=50K\n",
      "21     <=50K\n",
      "Name: loan, dtype: object \n",
      "\n",
      "(' Female', ' >50K')\n",
      "8      >50K\n",
      "19     >50K\n",
      "52     >50K\n",
      "67     >50K\n",
      "84     >50K\n",
      "Name: loan, dtype: object \n",
      "\n",
      "(' Male', ' <=50K')\n",
      "0      <=50K\n",
      "1      <=50K\n",
      "2      <=50K\n",
      "3      <=50K\n",
      "13     <=50K\n",
      "Name: loan, dtype: object \n",
      "\n",
      "(' Male', ' >50K')\n",
      "7      >50K\n",
      "9      >50K\n",
      "10     >50K\n",
      "11     >50K\n",
      "14     >50K\n",
      "Name: loan, dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "groups = xai.group_by_columns(df, [\"gender\", \"loan\"], categorical_cols=categorical_cols)\n",
    "for group, group_df in groups:\n",
    "    print(group)\n",
    "    print(group_df[\"loan\"].head(), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/scipy/stats/stats.py:245: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.\n",
      "  \"values. nan values will be ignored.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = xai.correlations(df, include_categorical=True, plot_type=\"matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = xai.correlations(df, include_categorical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of examples:  1200\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "proc_df = xai.normalize_numeric(bal_df)\n",
    "proc_df = xai.convert_categories(proc_df)\n",
    "x = proc_df.drop(\"loan\", axis=1)\n",
    "y = proc_df[\"loan\"]\n",
    "\n",
    "x_train, y_train, x_test, y_test, train_idx, test_idx = \\\n",
    "    xai.balanced_train_test_split(\n",
    "            x, y, \"gender\", \n",
    "            min_per_group=300,\n",
    "            max_per_group=300,\n",
    "            categorical_cols=categorical_cols)\n",
    "\n",
    "x_train_display = bal_df[train_idx]\n",
    "x_test_display = bal_df[test_idx]\n",
    "\n",
    "print(\"Total number of examples: \", x_test.shape[0])\n",
    "\n",
    "df_test = x_test_display.copy()\n",
    "df_test[\"loan\"] = y_test\n",
    "\n",
    "_= xai.imbalance_plot(df_test, \"gender\", \"loan\", categorical_cols=categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, mean_squared_error, roc_curve, auc\n",
    "\n",
    "from keras.layers import Input, Dense, Flatten, \\\n",
    "    Concatenate, concatenate, Dropout, Lambda\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers.embeddings import Embedding\n",
    "\n",
    "def build_model(X):\n",
    "    input_els = []\n",
    "    encoded_els = []\n",
    "    dtypes = list(zip(X.dtypes.index, map(str, X.dtypes)))\n",
    "    for k,dtype in dtypes:\n",
    "        input_els.append(Input(shape=(1,)))\n",
    "        if dtype == \"int8\":\n",
    "            e = Flatten()(Embedding(X[k].max()+1, 1)(input_els[-1]))\n",
    "        else:\n",
    "            e = input_els[-1]\n",
    "        encoded_els.append(e)\n",
    "    encoded_els = concatenate(encoded_els)\n",
    "\n",
    "    layer1 = Dropout(0.5)(Dense(100, activation=\"relu\")(encoded_els))\n",
    "    out = Dense(1, activation='sigmoid')(layer1)\n",
    "\n",
    "    # train model\n",
    "    model = Model(inputs=input_els, outputs=[out])\n",
    "    model.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "def f_in(X, m=None):\n",
    "    \"\"\"Preprocess input so it can be provided to a function\"\"\"\n",
    "    if m:\n",
    "        return [X.iloc[:m,i] for i in range(X.shape[1])]\n",
    "    else:\n",
    "        return [X.iloc[:,i] for i in range(X.shape[1])]\n",
    "\n",
    "def f_out(probs, threshold=0.5):\n",
    "    \"\"\"Convert probabilities into classes\"\"\"\n",
    "    return list((probs >= threshold).astype(int).T[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "50234/50234 [==============================] - 2s 31us/step - loss: 0.5588 - acc: 0.7166\n",
      "Epoch 2/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.4304 - acc: 0.7980\n",
      "Epoch 3/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3880 - acc: 0.8199\n",
      "Epoch 4/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3778 - acc: 0.8243\n",
      "Epoch 5/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3739 - acc: 0.8264\n",
      "Epoch 6/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3705 - acc: 0.8283\n",
      "Epoch 7/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3689 - acc: 0.8299\n",
      "Epoch 8/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3681 - acc: 0.8308\n",
      "Epoch 9/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3687 - acc: 0.8306\n",
      "Epoch 10/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3661 - acc: 0.8325\n",
      "Epoch 11/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3663 - acc: 0.8322\n",
      "Epoch 12/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3663 - acc: 0.8329\n",
      "Epoch 13/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3646 - acc: 0.8320\n",
      "Epoch 14/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3642 - acc: 0.8334\n",
      "Epoch 15/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3647 - acc: 0.8320\n",
      "Epoch 16/50\n",
      "50234/50234 [==============================] - 0s 8us/step - loss: 0.3639 - acc: 0.8323\n",
      "Epoch 17/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3643 - acc: 0.8332\n",
      "Epoch 18/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3639 - acc: 0.8324\n",
      "Epoch 19/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3632 - acc: 0.8334\n",
      "Epoch 20/50\n",
      "50234/50234 [==============================] - 0s 8us/step - loss: 0.3637 - acc: 0.8340\n",
      "Epoch 21/50\n",
      "50234/50234 [==============================] - 0s 8us/step - loss: 0.3635 - acc: 0.8346\n",
      "Epoch 22/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3627 - acc: 0.8344\n",
      "Epoch 23/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3628 - acc: 0.8342\n",
      "Epoch 24/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3628 - acc: 0.8343\n",
      "Epoch 25/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3621 - acc: 0.8342\n",
      "Epoch 26/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3622 - acc: 0.8348\n",
      "Epoch 27/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3629 - acc: 0.8343\n",
      "Epoch 28/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3628 - acc: 0.8344\n",
      "Epoch 29/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3623 - acc: 0.8345\n",
      "Epoch 30/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3619 - acc: 0.8347\n",
      "Epoch 31/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3616 - acc: 0.8345\n",
      "Epoch 32/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3617 - acc: 0.8352\n",
      "Epoch 33/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3612 - acc: 0.8336\n",
      "Epoch 34/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3606 - acc: 0.8342\n",
      "Epoch 35/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3616 - acc: 0.8350\n",
      "Epoch 36/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3608 - acc: 0.8347\n",
      "Epoch 37/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3603 - acc: 0.8358\n",
      "Epoch 38/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3605 - acc: 0.8358\n",
      "Epoch 39/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3607 - acc: 0.8358\n",
      "Epoch 40/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3605 - acc: 0.8347\n",
      "Epoch 41/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3602 - acc: 0.8350\n",
      "Epoch 42/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3600 - acc: 0.8357\n",
      "Epoch 43/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3598 - acc: 0.8359\n",
      "Epoch 44/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3601 - acc: 0.8345\n",
      "Epoch 45/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3597 - acc: 0.8351\n",
      "Epoch 46/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3598 - acc: 0.8359\n",
      "Epoch 47/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3599 - acc: 0.8355\n",
      "Epoch 48/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3598 - acc: 0.8339\n",
      "Epoch 49/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3592 - acc: 0.8353\n",
      "Epoch 50/50\n",
      "50234/50234 [==============================] - 0s 7us/step - loss: 0.3606 - acc: 0.8361\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6958645a90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model(x_train)\n",
    "\n",
    "model.fit(f_in(x_train), y_train, epochs=50, batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 0s 231us/step\n",
      "Error 0.3455: \n",
      "Accuracy 83.7500: \n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(f_in(x_test), y_test, verbose=1)\n",
    "print(\"Error %.4f: \" % score[0])\n",
    "print(\"Accuracy %.4f: \" % (score[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities = model.predict(f_in(x_test))\n",
    "pred = f_out(probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAI6MQAgAAABiZnYYOAACwNTfffHM2bNiQG2+8cegog1m1alXWrFmTnXfeeegoAMCMUAgBAPdoGzZsyOrVq7N27dpU1dBxVlx3Z9OmTdmwYUP233//oeMAADPClDEA4B7txhtvzB577DHKMihJqip77LHHqO+QAgCmTyEEANzjjbUM2mLsf38AYPoUQgAAW/HNb34zb33rW5f9dc4///x88pOfXPbXAQBIrCEEAGxn1p72gak+39WvffJWH99SCJ100klLer7uTndnhx3u3O/dzj///Oy666559KMffae+DgDgrnCHEADAVpx22mn5j//4jxx66KE59dRT8/jHPz6HHXZYHvawh+X9739/kuTqq6/OQx/60Jx00kk57LDDcu211+btb397DjzwwBx99NF54QtfmJNPPjlJsnHjxjzzmc/M4YcfnsMPPzyf+MQncvXVV+f000/PG9/4xhx66KH5+Mc/PuRfGQAYAXcIAQBsxWtf+9pceumlufjii7N58+Z873vfy2677Zavf/3rOfLII3P88ccnSa644oqceeaZeetb35rrrrsur3nNa/LZz342q1evzjHHHJOHP/zhSZJTTjklp556ao466qh86UtfyhOf+MRcfvnledGLXpRdd901L3/5y4f86wIAI6EQAgBYou7OK17xinzsYx/LDjvskC9/+cv56le/miR54AMfmCOPPDJJ8ulPfzqPfexjc//73z9J8qxnPSv/9m//liT58Ic/nMsuu+zW5/z2t7+dG264YYX/JgDA2CmEAACW6F3velc2btyYCy+8MDvvvHPWrl1763bw973vfW+9rrvv8DluueWWXHDBBbn3ve+97HkBAO6INYQAALZi9erVt97B861vfSt77713dt5555x33nm55pprFv2aRz3qUfnoRz+a66+/Pps3b8773ve+Wx97whOekD/6oz+69fjiiy++3esAACw3hRAAwFbsscceecxjHpODDz44F198cdavX59169blXe96Vx7ykIcs+jX77LNPXvGKV+SII47Isccem4MOOij3u9/9kiRvfvObs379+hxyyCE56KCDcvrppydJnvrUp+Zv//ZvLSoNAKwIU8YAgO3KtraJXw7vfve7t3nNpZdeepvj5z73uTnxxBOzefPmPOMZz8gTnvCEJMmee+6Zv/qrv7rd1x944IG55JJLphMYAGAb3CEEALAMXvWqV+XQQw/NwQcfnP333z9Pf/rTh44EAHArdwgBACyDN7zhDUNHAAC4Q+4QAgAAABgZhRAAcI+3tW3cx2Dsf38AYPoUQgDAPdqqVauyadOm0ZYi3Z1NmzZl1apVQ0cBAGaINYQAgHu0NWvWZMOGDdm4cePQUQazatWqrFmzZugYAMAMWVIhVFXHJXlTkh2T/Gl3v3bB4/dK8udJHplkU5Jnd/fV040KAIzRzjvvnP3333/oGAAAM2WbU8aqasckb0nypCQHJTmhqg5acNkLklzf3T+a5I1Jfm/aQQEAAACYjqWsIfSoJFd291XdfVOS9yR52oJrnpbk/04+/+skj6+qml5MAAAAAKZlKYXQPkmunXe8YXJu0Wu6e3OSbyXZYxoBAQAAAJiupawhtNidPgu3+VjKNamqE5OcODn8TlVdsYTXZ3r2TPL1oUPcFWUSIktnnDMGxjljYJwzBsY5Y2Ccr7wHLuWipRRCG5LsO+94TZLr7uCaDVW1U5L7JfnGwifq7jOSnLGUYExfVa3v7nVD54DlZJwzBsY5Y2CcMwbGOWNgnN9zLWXK2GeSHFBV+1fVLkmek+TsBdecneQXJp//VJJ/6u7b3SEEAAAAwPC2eYdQd2+uqpOTnJu5bef/rLs/X1WvTrK+u89O8vYk76yqKzN3Z9BzljM0AAAAAHfdUqaMpbvPSXLOgnOvnPf5jUmeNd1oLAPT9RgD45wxMM4ZA+OcMTDOGQPj/B6qzOwCAAAAGJelrCEEAAAAwAxRCAEAAACMjEIIAAAAYGQUQgCwHaiqg4fOAMDdV1VHVtXqecerq+qIITPBcquqXYfOwO1ZVHrGVdW9kjwzydrM21Wuu189VCaYlqr6f0nu8D+x7j5+BePAsqqqf06yS5J3JHl3d39z2EQwfVX1viR/luQfuvuWofPAcqiqi5Ic1pM3YlW1Q5L13X3YsMlg+VTVl7p7v6FzcFtL2nae7dr7k3wryYVJ/mvgLDBtbxg6AKyU7j6qqg5I8otJ1lfVp5Oc2d0fGjgaTNPbkjw/yZur6r1J3tHdXxg4E0xb9bzfynf3LVXlfRnbvap62R09lMQdQvdA7hCacVV1aXebZgAwI6pqxyRPT/LmJN/O3A9Zr+juvxk0GExRVd0vyQlJfivJtUn+JMlfdPfNgwaDKaiqv0lyfuYK0CQ5Kcnjuvvpg4WCKaiqG5O8PsnmRR4+tbt3X+FIbINCaMZV1RlJ/rC7Pzd0Fpi2qvpctj5l7JAVjAPLqqoOydydE09O8qEkb+/uz1bVDye5oLsfOGhAmJKq2iPJzyb5uSTXJXlXkqOSPKy7jx4wGkxFVe2duVL/mMz9HPORJC/t7q8NGgzupqr6ZJKXdPeFizx2bXfvO0AstkIhNOOq6rIkP5rki5mbMlZJ2htlZkFVbfUNcHdfs1JZYLlV1ccyd5fEX3f39xc89nPd/c5hksH0TO6ceEiSd2ZuuthX5j22vrvXDRYOgK2aTPV9cZKf6e43LXjsf3T3V4dJxh1RCM24O3rD7I0ywPalql7a3X+w4NwpC3/ggu1ZVR3T3f80dA5YTlV1Zha5w7m7f3GAODA1VfX5JD+Z5OwkR2fuZoRbdfc3BojFVth2fsZNip/dkzx18rG7MohZM9m+9TNV9Z2quqmqflBV3x46F0zZzy9y7nkrHQKW2UOr6tY1Jqrqh6rqpCEDwTL4+yQfmHx8JMluSb4zaCKYjj9O8o+Zu9PzwgUf6wfMxR1wh9CMq6pTkrwwyZbFRp+R5Izu/sPhUsF0VdX6JM9J8t4k6zL3xvlHu/u3Bg0GU1BVJyR5bubWUPn4vIdWJ/lBdx87SDBYBlV1cXcfuuDcRd39iKEywXKbbDv/4e4+ZugsMA1V9bbufvHQOdg22xvOvhckOaK7v5skVfV7SS5IohBipnT3lVW1Y3f/IMmZk0XtYBZ8MslXkuyZ5P/MO39DkksGSQTLZ4equnVL7smuersMnAmW2wFJ9hs6BEyLMmj7oRCafZXkB/OOf5AFczlhBnyvqnZJcnFVvS5zb57vO3AmmIrJNN9rkvzY0FlgBZyb5KyqOj1za6y8KHPTD2BmVNUN+e81hDrJV5P8xnCJgLEyZWzGVdXLkvxCkr+dnHp65nbt+IM7/irYvkwWT/9q5n6LfGqS+yV5a3dfOWgwmIKq+ufuPmrBG4jkv3eN3G2gaDB1k6kzv5zk8Zkb4x9M8qeTuz9hZlTV/TN3Z9Cqyanu7o8NGAkYIYXQCFTVYZlbe6KSfKy7Lxo4EkxVVd03yfe7+5bJ8Y5J7tXd3xs2GQDAbVXVLyU5JcmaJBcnOTLJBdYQAlaaXcZmVFXtNvnz/kmuTvIXSd6Z5JrJOZglH0lyn3nH907y4YGywLKY7Ka3et7xrlV1xJCZYNqq6oCq+uuquqyqrtryMXQumLJTkhye5JruflySRyTZOGwkYIysITS73p3kKZnb4u92UwySPGiIULBMVnX3rdu1dvd3quo+W/sC2A69Lclh846/t8g52N6dmeR3k7wxyeOSPD/WPmT23NjdN1ZVqupe3f2Fqnrw0KGA8VEIzajufsrkz/2HzgIr4LtVdVh3fzZJquqRSb4/cCaYtlt3XkqS7r6lqnwfZ9bcu7s/Mtlp7Jokr6qqj2euJIJZsaGqdk/yd0k+VFXXJ7lu4EzACPlBcsZV1WOSXNzd362qn83cb5L/oLu/NHA0mKaXJnlvVW35YeoBSZ49YB5YDldV1a9m7q6gJDkpiak0zJobJwtL/3tVnZzky0n2HjgTTFV3P2Py6auq6rzMbYZhNz1gxVlUesZV1SVJHp7kkMytIfT2JP+rux87aDCYsqraOcmDMze14AvdffPAkWCqqmrvJG9Ockzmpv5+JMlLu/trgwaDKaqqw5NcnmT3JK9JsluS13f3pwYNBgAzSCE046rqs919WFW9MsmXu/vtW84NnQ2mZbJe0MuSPLC7X1hVByR5cHf//cDRAFiiyQ6Rr+3uXx86CwCMgSljs++GqvrNJD+b5McnP2ztPHAmmLYzM7eA+o9NjjckeW8ShRDbvar6je5+XVX9YW67SUCSpLt/dYBYMHXd/YOqeuRk/SC/sQSAZaYQmn3PTvLcJC/o7v+sqv2SvH7gTDBtP9Ldz66qE5Kku79fVXalYVZcNvlz/aApYGVclOT9VfXeJN/dcrK7/2a4SAAwmxRCM667/zPJ7887/lKSPx8uESyLm6rq3pncPVFVP5Lkv4aNBFPz7Mzd7bZ7d79p6DCwzO6fZFPm1sraopMohABgyqwhNKOq6p+7+6iquiG3nWJQSbq7dxsoGkzV5E6gn0vygiQHJflgksckeV53nz9gNJiKqrosyZOSnJ3k6Mz9P36r7v7GALEAANjOKYSA7V5VXZjkCUmOzNyb5U9199eHTQXTMdlq/sVJHpS5LbjnF0Ld3Q8aJBgsg6o6M4uvlfWLA8QBgJmmEJpxVXVkks939w2T412T/M/u/pdhk8H0VNVbkryjuz8zdBZYLlX1tu5+8dA5YDlV1TPnHa5K8owk11k8HQCmTyE046rqoiSHbdmto6p2SLLetvPMksmUmgOTXJO5RUi3TI08ZNBgMAVVtVt3f7uq7r/Y46aMMcsmP7d8uLuP2ebFAMCdYlHp2XebrVu7+5aq8u/OrHnS0AFgGb07yVOSXJi5qTS3mTKWualkMKsOSLLf0CEAYBYpBmbfVZP1J942OT4pyVUD5oGp6+5rhs4Ay6W7nzL5c/+hs8ByW2QzjP9M8r8HigMAM82UsRlXVXsneXPmtm/tJB9J8tLu/tqgwQC4U6rqGUn+qbu/NTnePcnR3f13wyYDAGB7pBACgO1AVV3c3YcuOHdRdz9iqEwwbYpPAFg5OwwdgOVVVQdW1Ueq6tLJ8SFV9dtD5wLgTlvse7ap38ya391SBiVJd38zye8OmAcAZpZCaPb9SZLfTHJzknT3JUmeM2giAO6K9VX1+1X1I1X1oKp6Y+YWmoZZovgEgBWiEJp99+nuTy84t3mQJADcHS9JclOSv0pyVpLvJ/mVQRPB9Ck+AWCF+I3L7Pt6Vf1IJjt2VNVPJfnKsJEAuLO6+7tJTquqXbv7O0PngWXykiS/k7niM0k+mMRUdwBYBhaVnnFV9aAkZyR5dJLrk3wxyc/Yphtg+1JVj07yp0l27e79qurhSX65u08aOBoAANshU8ZmWFXtkGRddx+bZK8kD+nuo5RBANulNyZ5YpJNSdLd/5rkxwdNBFNWVR+a7Cy25fiHqurcITMBwKxSCM2w7r4lycmTz7/b3TcMHAmAu6G7r11w6geDBIHls+dkZ7EkSXdfn2TvAfMAwMxSCM2+D1XVy6tq36q6/5aPoUMBcKddO5k21lW1S1W9PMnlQ4eCKbulqvbbclBVazNZBxEAmC5rCM24qvpiFvlBqrsfNEAcAO6iqtozyZuSHJu5X+icm+SU7t40aDCYoqo6LnNrH350curHk5zY3aaNAcCUKYRmXFXdO8lJSY7KXDH08SSnd/f3Bw0GALCIqto7yYlJLk6yKsnXuvtjw6YCgNmjEJpxVXVWkm8nedfk1AlJdu/unx4uFQB31mTXyDclOTJzBf8FSU7t7qsGDQZTVFW/lOSUJGsyVwgdmeSC7j5m0GAAMIOsITT7Htzdv9Td500+Tkzy4KFDAXCnvTvJWUkekOSHk7w3yV8Omgim75Qkhye5prsfl+QRSTYOGwkAZpNCaPZdVFVHbjmoqiOSfGLAPADcNdXd7+zuzZOPv4jFdpk9N3b3jUlSVffq7i/EL7IAYFnsNHQAlt0RSX6+qr40Od4vyeVV9bkk3d2HDBcNgDvhvKo6Lcl7MlcEPTvJB7bsHNnd3xgyHEzJhqraPcnfZW6n1OuTXDdwJgCYSdYQmnFV9cCtPd7j2faOAAAA4UlEQVTd16xUFgDuusmukVts+eZdW47tHsmsqarHJrlfkn/s7puGzgMAs0YhBADbgar66cy9Mf52Vf1OksOSvKa7PztwNAAAtkPWEAKA7cNvT8qgo5L8RJJ3JHnbsJEAANheKYQAYPvwg8mfT05yene/P8kuA+YBAGA7phACgO3Dl6vqj5P8dJJzqupe8X0cAIC7yBpCALAdqKr7JDkuyee6+9+r6gFJHtbdHxw4GgAA2yGFEAAAAMDIuNUcAAAAYGQUQgAAAAAjoxACAAAAGBmFEAAAAMDIKIQAAAAARub/A6C98DTGelThAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_= xai.metrics_plot(\n",
    "        y_test, \n",
    "        probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship ethnicity   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family     White     Male          2174   \n",
       "1     Exec-managerial         Husband     White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family     White     Male             0   \n",
       "3   Handlers-cleaners         Husband     Black     Male             0   \n",
       "4      Prof-specialty            Wife     Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week    loan  \n",
       "0             0              40   <=50K  \n",
       "1             0              13   <=50K  \n",
       "2             0              40   <=50K  \n",
       "3             0              40   <=50K  \n",
       "4             0              40   <=50K  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 15)\n",
      "(16, 15)\n",
      "(66, 15)\n",
      "(4, 15)\n",
      "(506, 15)\n",
      "(3, 15)\n",
      "(19, 15)\n",
      "(39, 15)\n",
      "(2, 15)\n",
      "(537, 15)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = xai.metrics_plot(\n",
    "    y_test, \n",
    "    probabilities, \n",
    "    df=x_test_display, \n",
    "    cross_cols=[\"gender\", \"ethnicity\"],\n",
    "    categorical_cols=categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['workclass', 'education', 'marital-status', 'occupation',\n",
      "       'relationship', 'ethnicity', 'gender', 'loan', 'target'],\n",
      "      dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n",
      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['workclass', 'education', 'marital-status', 'occupation',\n",
      "       'relationship', 'ethnicity', 'gender', 'loan', 'target'],\n",
      "      dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n",
      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['workclass', 'education', 'marital-status', 'occupation',\n",
      "       'relationship', 'ethnicity', 'gender', 'loan', 'target'],\n",
      "      dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = [xai.metrics_plot(\n",
    "    y_test, \n",
    "    probabilities, \n",
    "    df=x_test_display, \n",
    "    cross_cols=[p],\n",
    "    categorical_cols=categorical_cols) for p in protected]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xai.confusion_matrix_plot(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xai.confusion_matrix_plot(y_test, pred, scaled=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = xai.roc_plot(y_test, probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = [xai.roc_plot(\n",
    "    y_test, \n",
    "    probabilities, \n",
    "    df=x_test_display, \n",
    "    cross_cols=[p],\n",
    "    categorical_cols=categorical_cols) for p in protected]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_= xai.pr_plot(y_test, probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = [xai.pr_plot(\n",
    "    y_test, \n",
    "    probabilities, \n",
    "    df=x_test_display, \n",
    "    cross_cols=[p],\n",
    "    categorical_cols=categorical_cols) for p in protected]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['target', 'manual-review'], dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = xai.smile_imbalance(\n",
    "    y_test, \n",
    "    probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6958641828>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[[\"correct\", \"incorrect\"]].sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['target', 'manual-review'], dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = xai.smile_imbalance(\n",
    "    y_test, \n",
    "    probabilities,\n",
    "    threshold=0.75,\n",
    "    display_breakdown=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6958645ba8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rrZoBS9D39kzbpwMPANj+/4x8YZXozx/K5SWB5qMxFU14teBB23ev1pbnc3w2t70AeBiaVXiBh9otaXAS9L39sYyBGpoTVGgmE2N8viPp3cCTJL0c+ALwlZZr6rKrJb0eWFfSLEmfAL7fdlEdda+kzXjkvb4nsPof0c7KZGwPkg4A3gPsBHyTZtjhr2wvarOurpK0DnAscADNJ6NvAGc7L8JxKXNI76F5PqF5Pj9gO0eGjZGkFwFnAs8DrgZmAEfaXtpqYQOSoB9F+Su/J00wXW57sBcMXotIOgK4yHY+FQ2ApF1t/7jtOmpRhhJ3oHmvX1eOpa9Chm56KEcwHAAssn1hQn7CDgV+Lumzkl5R3lgxfh+V9DNJ75f03LaL6TJJP6FZluN+21fXFPKQoB/NPwD7AD+V9AVJR0p6YttFdZXtY4Bn0YzNvx74haSz262qu2zvC7wMWAnMk7RM0v9pt6rOOhR4EFgg6UpJ/1vS09sualAydNOHcqr+fsBxwIG2q1rZbqqVU80PpDmzcx/bM1ouqfMk7UzTI32N7fXbrqfLJM0C/i/wBtvrtl3PIOSj8yjKUTevpDl9/4XA/HYr6i5JBwKvBfYFFgFnA69us6Yuk7QjzevySOAO4PPAO1otqsMkzaR5Pb6G5tDKk9qsZ5DSo+9B0nnAHjRnHi6gGat/uN2qukvS52nC6GuZkJ04SZcDnwO+YPuWtuvpMkk/pFkQ7gvAebZvaLmkgUrQ91B6oBfbrubEiYhYk6Tn2P5Z23VMlgT9CCTtZ/tSSX8x0uO2/2Oqa+oySd+1/RJJq3j0mZsCnDmPsZG0wParJS1j5Ofz+S2V1jmSjrL9b5LePtLjtj861TVNhozRj+ylwKU0Y/OrM5CgHwPbLym3ucjIYJxQbg9ptYo6bFBuR3ptVtMLTo++B0nb2b5xtLboj6TP2j56tLboj6TTbL9ztLYYnaS9bX9vtLauynH0vX1phLYvTnkV9XjUST3lhKkXtVRLDV4+QttBU15FHT7RZ1snZehmBJKeQxNKm6w2Tr8xkBOmxqhcPnBoMbOhq/aIZgnoea0V1lGS3gy8Bdhe0vC1WDYCquiBThVJe9EsQz5jtXH6jYEqjqGHBP1j2YFm/HM6jx6nX0Vz0lSMge0PAh+U9EHb72q7ngqcC3wN+CBw8rD2Vbmu8ZitD2xIk4XDx+nvoTk/oQoZo+9B0l62f9B2HTUpl2acxbBPRrYva6+i7pG0se17JG060uMJ+7GT9Azbv2y7jsmSoB+BpJNsn17W917jCbL91hbK6jxJ/53miJFtaC6BtyfwA9v7tVpYx0i60PYhkm6keX0OvxiObW/fUmmdJWkGzZmwz+XRnZAqXpsZuhnZteV2catV1OcEYDea5Z73LXMhp7ZcU+fYPqTc5rKBg/PvwHk0Q7Z/TXO92JWtVjRA6dH3qVw0Y0Pb94y6c4xI0pW2d5N0FbCH7T9Iusr2Lm3X1kWS9gausn2vpKNo1mL6mO1ftVxa50haYvtFkpYOnXAm6Tu2X9p2bYOQwyt7kHSupI0lbQD8FLhO0olt19VhKyRNB74MXCzpAiBrtIzfWcB9kl5AM+zwS+Cz7ZbUWUPrz99arpWwK80QYxXSo+9hqLcp6Q00x3u/E1iSU8wnTtJLgU2Arw+/YHj0T9KPbL9Q0t8Cv7Z9zlBb27V1jaRDgP8EtqU5fn5j4FTbC1stbEAyRt/bemXt9MOBf7T9gKT8ZRyn1Y4SWVZu83yO36pyjsLRwD7lugnrtVxTJ9m+sNy9m2YZ7aok6Hv7JHAT8BPgMknPoDm+NsbnRzQ9prtojhSZTvNR+XbgONtL2iyug15Dc6WuN9n+Tbki0odbrqmTJJ05QvPdwGLbF0x1PYOWoZsxkjTN9oNt19FFkv4ZON/2N8r2ATRXmloAfNz2Hm3W10WStqQ5kgngCtu3t1lPV0maBzyHZj16gL8ErqHpmNxg+21t1TYICfoeJG0CvBf4s9L0HeB9tu9ur6rukrTY9uyR2nL0zdhJejVND34RzSekfYATbWc9pjGSdClwwFAnrqzD9E2a9YSW2d6pzfomKkM3vX0KuJpHLnd3NPBpYMR16mNUd0p6J81VpqAZerirjC3nyl1j9x5gt6FefDnp51tk4b3x2JpmyeKhTtwGwNNsPySp81dDS9D39kzbfzls+9RyDHiMz+tpPiF9uWx/t7StS64dOx7rrDZUcwc5ZHq8TgeukrSI5tPRnwF/Xw6t/labhQ1Chm56kPQDmo/C3y3bewMfsb1Xu5V1m6QNbf++7Tq6TtKHgefTXDcWmk9IS7Me/fhI2grYnSbor6jpOrwJ+h4k7QLMpzneW8CdwBzbS3t+Y4xI0ouBs2nOMH56OdHnf9h+S8uldVZZRvslNK/Py2yf33JJnSRJwBuA7W2/rxzB9FTbV7Rc2kAk6PsgaWOALH8wMZJ+SLP060Lbu5a2q20/r93KukvSU4E9aOY4rrT9m5ZL6iRJZ9E8h/vZ3rGssvpN27uN8q2dkPG8HiRtVo6vXQR8W9LHJW3WclmdZvvm1ZoeaqWQCpTVQK8AjqD5A3q5pDe1W1Vn7WH7eOB+ANt30axVX4VMxvb2eeAymmNqoflodx7w561V1G03l+EbS1ofeCuPrBQaY3cisKvtO6DpmADfpzlaLMbmgXL0l+FPRzBVcyRYevS9bWr7/bZvLF8foDmbM8bnr4HjaQ5lWwHsUrZjfFbQXPVsyCpg9U9M0Z8zgfOBLST9Hc0RYX/fbkmDkzH6HiR9hGZN+gWl6Ujgubbf215VEQ1JnwF2Bi6g6YkeRjOU83MA2x9tr7ruKddH2J9mYvsS29V82kzQ9yBpFc2JEw/R/OevA9xbHrbtjduqrYvKx+HjgJkMGza0nXHlcZDUs8NhOxd1GYMydLMlj35tVrG2f4I+poyk79MsBbuEYZOwtr/UWlGVkPTUHHEzfpL+J83JfLfxSMfOtSxJnqDvk6RTbJ/Sdh1dlvVsJk/WoZ8YSctpjry5o+1aJkMmY/t3aNsFVOBCSQe3XUSlNPou0cPNPLLOTXVyeGX/8kaauBOAd5dFooaWes5cx2D8S9sFdNwNwCJJXwX+tIhZLRPaCfr+vajtArrO9kZt11AbSS8BZtn+pzLZvaHtG9uuq4N+Vb7Wp6ITpYZkjL4HSc+muQDzlrafJ+n5wKHlePqYgMx5TFw56mY2sIPtZ0t6GvAF23u3XFqn1TixnTH63v4FeBflCvFlMbPXtlpRPTLnMXFH0DyP9wKU1RbzqWniLmq7gEFL0Pf25BFWr8tlBAcjcx4T90c3H8mHTtvfoOV6alHdazNB39tvJT2TR95IRwK3tltSNTLnMXELJH0SmC7pOJoLZGRSduKqew4zRt+DpO2BecCLgbuAG4GjbN/UZl1dlTmPwZP0cuAAml7oN2xf3HJJnTVsYvvTtU1sJ+j7UD4Sr2N71ag7x2OS9B2aFRc/mfXoJ668Lu8v1zXdAdgB+JrtB1ourXNqn9jO4ZU9SPrb1bYBsP2+VgrqvifbvmLoeSwy5zF+lwH7lItkfItmAb7X0CynHWNzBLAr8CNoJrYlVTOxnTH63u4d9vUQcBDNglwxPpnzGCzZvg/4C+ATto8Admq5pq6qemI7PfoebP/D8O2ybPHClsqpwfE0cx7PkfRrypxHuyV1miTtRdODP7a05T09PqtPbL+JiiZlM0Y/BuUj8hW2Z7VdS5dlzmMwJL0UeAfwPdunlYMH3mb7rS2X1kk1T2wn6HuQtIzyUQ5YF5gBvM/2P7ZXVXetPucxJHMe0bbaJ7bzMa+3Q4bdfxC4zXYmD8fv3mH3n0jz/FZzFZ+pIukrPNIBWYPtnHU8dlVPbKdH/xgkrQMszaF/k0fSE4CFtv9b27V0SRmyeUy2vzNVtdRiaD3/cgGSJ9k+XdKPhw4D7rr06B+D7Ycl/UTS02u5nNjj0JOB7dsuomsS5JOi6ontan6RSbIVcI2kKxg27JCPxuPzWHMe7VXUbZJmAR+kOaTyiUPttvPHc+zeRrOA4fm2rykT299uuaaBydBNDyXgTxzeBJxme4+WSuo0Sc8Ytpk5jwmS9F2a65yeAbwSOIbmPd3zouGx9knQ9zDSdTglLa3lgsFTKXMegydpie0XSVpme+fS9p+292m7tq5YWya2M3QzAklvBt4CbC9p6bCHNgK+105V3ZY5j0lxf/kDer2kvwF+DWzRck1d85G2C5gK6dGPQNImwFNoxj9PHvbQKtt3tlNV90m6FNgNyJzHBEj6rO2jJZ0E/BMwHXg/sAlwuu3LWy0wHncS9DFlMucxGJJ+SrPu0kLgZax2oYx0Rsau9ontDN3EVJq2+qGBkp7UVjEd9s/A12kOTV1CE/QedltFOE2xT/PIxPa+lIntVisaoPToY9INn/MAfjHsoY1o1mnJwmbjIOks229uu44a1D6xnR59TIVzga+ROY+BSsgPVNUT2+nRR8Raa22Z2E7QR8Raa22Z2M7QTUSszdaKie306CNirVf7xHaCPiKicrk4eERE5RL0ERGVS9BHRFQuQR8RUbn/Ald6XcX2YhwWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_bars = [\"true-positives\", \"true-negatives\", \n",
    "                \"false-positives\", \"false-negatives\"]\n",
    "d[display_bars].sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['target', 'manual-review'], dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = xai.smile_imbalance(\n",
    "    y_test, \n",
    "    probabilities,\n",
    "    bins=9,\n",
    "    threshold=0.75,\n",
    "    manual_review=0.00001,\n",
    "    display_breakdown=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f69586487f0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[[\"correct\", \"incorrect\", \"manual-review\"]].sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>0.044083</td>\n",
       "      <td>0.089583</td>\n",
       "      <td>0.018417</td>\n",
       "      <td>0.02575</td>\n",
       "      <td>0.002417</td>\n",
       "      <td>0.001167</td>\n",
       "      <td>0.040667</td>\n",
       "      <td>0.003083</td>\n",
       "      <td>0.011917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  workclass  education  education-num  marital-status  occupation  \\\n",
       "0  0.030583     0.0045  -0.000417       0.044083        0.089583    0.018417   \n",
       "\n",
       "   relationship  ethnicity    gender  capital-gain  capital-loss  \\\n",
       "0       0.02575   0.002417  0.001167      0.040667      0.003083   \n",
       "\n",
       "   hours-per-week  \n",
       "0        0.011917  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_avg(x, y):\n",
    "    return model.evaluate(f_in(x), y, verbose=0)[1]\n",
    "\n",
    "imp = xai.feature_importance(x_test, y_test, get_avg)\n",
    "\n",
    "imp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
